Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning

Cognitive tasks are organized by shared and specialized neural processes. Masked fMRI reconstruction provides a common self-supervised objective for quantifying transfer relations among task states, but existing reconstruction-based taskonomies mainly study one-to-one transfer from a single source task to a target. Here, we extend an fMRI cognitive taskonomy from single-source to multi-source transfer across 23 Human Connectome Project task states and use Boolean Integer Programming (BIP) to analyze budget-constrained task allocation. We train 1,127 task-specific and transfer models. Single-source transfer is directional and paradigm structured: motor states transfer well within the motor paradigm but provide limited support to most non-motor targets, consistent with a shared sensorimotor execution system and effector-specific representations. Multi-source transfer depends on the composition of the source set, suggesting that many-to-one task relations are not fully captured by pairwise taskonomy alone. Across supervision budgets, BIP repeatedly allocates direct supervision to several 0-back and 2-back working-memory states, although these states are not consistently the strongest individual sources. This pattern may reflect the integration of perceptual, attentional, and executive processes in working-memory tasks. Together, these findings reveal a cross-paradigm-limited motor cluster and working-memory states with high priority under the specified global allocation objective. Our study extends reconstruction-based fMRI taskonomy from one-to-one transfer to many-to-one task relations and budget-constrained task dependencies.

Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity

While NLP models significantly impact our lives, there are rising concerns about privacy invasion. Although federated learning enhances privacy, attackers may recover private training data by exploiting model parameters and gradients. Therefore, protecting against such embedding attacks remains an open challenge. To address this, we propose Subword Embedding from Bytes (SEB) and encode subwords to byte sequences using deep neural networks, making input text recovery harder. Importantly, our method requires a smaller memory with $256$ bytes of vocabulary while keeping efficiency with the same input length. Thus, our solution outperforms conventional approaches by preserving privacy without sacrificing efficiency or accuracy. Our experiments show SEB can effectively protect against embedding-based attacks from recovering original sentences in federated learning. Meanwhile, we verify that SEB obtains comparable and even better results over standard subword embedding methods in machine translation, sentiment analysis, and language modeling with even lower time and space complexity.

Retrieval Augmented Generation for Domain-specific Question Answering

Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for a specific domain, such as finance, healthcare, education, and customer service for a product. To better cater to domain-specific understanding, we build an in-house question-answering system for Adobe products. We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. We showcase that fine-tuning the retriever leads to major improvements in the final generation. Our overall approach reduces hallucinations during generation while keeping in context the latest retrieval information for contextual grounding.